# ⚡ RAID — Research Architecture for Intraday Data

UQO Working Paper No. 40 — A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics

TL;DR — RAID asks whether heterogeneous high-frequency econometric workloads (realized measures, rolling/batched regressions, HAR, GARCH-family QML, block bootstrap) can be turned into batched, fused, streaming computation on Apple Silicon without losing estimator accuracy, inference validity or reproducibility. It is a C++20 library with a FP64 CPU reference backend, an optional FP32 Metal backend (Apple GPUs have no FP64), nanobind Python bindings, a CLI, a FinData OS client, and a benchmark protocol that reports break-even points and the cases where the CPU wins. Status: Phase 1 (foundation) — see RESEARCH_STATUS.md. No performance numbers are claimed yet.

# 📌 Status

# 🎯 What this paper does

Three falsifiable questions. RQ1 At what data and batch sizes does Metal beat an optimized multicore CPU, once I/O, shader compilation, synchronization and transfers are counted? RQ2 Does batched estimation raise throughput while preserving statistical coverage and convergence? RQ3 Can an Arrow/Parquet-to-estimator pipeline materially reduce copies, memory pressure and end-to-end latency? Negative results are published. Tier A methods (returns, calendar-aware aggregation, RV/BPV/RSV/RQ/jumps/rCov, OLS/WLS/ridge with HC/HAC/cluster inference, HAR family, GARCH/GJR/EGARCH with Gaussian/Student-t QML, block bootstrap) are validated against R highfrequency/rugarch and Python statsmodels/arch; GPU results are validated against the FP64 CPU reference with per-method tolerances.

# 🏆 Headline results

None yet — benchmarks (E1–E7) run in Phase 6; every number in the paper will point to a record under experiments/results/ listed in experiments/manifests/.

# 🗂 Repository layout

wp40_raid/
├── include/raid/{core,data,statistics,estimators,models,backend}/   # public C++20 headers
├── src/…  backends/cpu/  backends/metal/{host,shaders}/             # implementations (Metal: Phase 3)
├── bindings/python/ python/raid/ cli/                                # nanobind module, Python API, `raid` CLI
├── tests/{unit,property,reference,simulation,stress,integration}/   # Catch2 (reference = vs statsmodels/arch/highfrequency)
├── benchmarks/ experiments/{configs,scripts,results,manifests}/     # harness + tracked artifacts (Phase 6)
├── datasets/{synthetic,fixtures}/                                    # synthetic only; real data cached outside git
├── docs/ paper/ scripts/ .github/workflows/

# 🚀 Quick start

brew install cmake ninja eigen apache-arrow catch2 nlohmann-json          # macOS
cmake --preset macos-release && cmake --build --preset macos-release      # CPU + Metal + Arrow
ctest --preset macos-release                                               # unit + reference tests
./build/macos-release/raid devices
./build/macos-release/raid validate datasets/fixtures/synthetic_bars_1m.csv
./build/macos-release/raid estimate realized datasets/fixtures/synthetic_bars_1m.csv --backend auto
uv venv --python 3.12 .venv && uv pip install --python .venv/bin/python -e ".[dev]"   # Python package (nanobind)

Linux / CPU-only: cmake --preset cpu-only && cmake --build --preset cpu-only && ctest --preset cpu-only.

# 🗃 Data

Market data come from the owner's FinData OS lakehouse (https://findata.spboucher.ai/api/v1, FirstRate Data under a private-use license): 1-minute OHLCV bars (bar-open timestamps, extended 04:00–20:00 ET, regular 09:30–16:00 with the 16:00 closing print, zero-volume minutes absent at the source), dataset versions pinned via X-Dataset-Version. Raw bars are never committed; datasets/fixtures/ contains synthetic bars generated by scripts/make_fixtures.py.

# 🔬 Methodology

See docs/methods/registry.md (one entry per method: equations, conventions, reference software and tolerance) and docs/architecture/overview.md.

# 🖼 Figures & tables inventory

To be generated in Phase 6–7 from experiments/results/.

# ⚠️ Limitations

Apple Metal has no FP64: GPU paths are FP32 with compensated reductions and FP64 combination on the CPU, and are only valid where the documented tolerance is met. No tick/quote data: microstructure-noise and order-flow methods are out of scope. Energy is reported only when measured.

# 📖 Citation

@techreport{boucher2026raid,
  title  = {RAID: A Metal-Accelerated C++ Framework for Reproducible High-Frequency Financial Econometrics},
  author = {Boucher, Simon-Pierre},
  institution = {Université du Québec en Outaouais, Département des sciences administratives},
  type   = {UQO Working Paper}, number = {40}, year = {2026}
}

# 👤 Author & contact

Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca — Département des sciences administratives, UQO.

# 📝 Notes

Directory name wp40_raid (charter requirement) instead of the usual slug; git remote gitsrv:srv/git/wp40_uqo.git.